This slide will explain about building blocks of JVM optimization for you java based application.
It explains basics of heap concepts and different type of java garbage collectors.
Вячеслав Блинов «Java Garbage Collection: A Performance Impact»Anna Shymchenko
This document discusses Java garbage collection and provides an overview of common GC algorithms, their performance impacts, and basic tuning strategies. It describes how the generational heap is divided and explains that GC pauses can significantly impact performance. Different algorithms like the serial, parallel, CMS and G1 collectors are introduced along with considerations for choosing a collector based on heap size, CPU usage, and pause requirements. Guidelines are provided for sizing the heap and generations as well as enabling adaptive sizing.
As service providers and primary code contributors in the Islandora Community, discoverygarden encounters customers who are ingesting, accessing, and storing high volumes of data. For example, a customer who had 150,000 objects in 2012 now has three million objects and expectations to grow to five million in the very short term. This is increasingly common.
As repositories grow in size they can encounter poor performance, particularly during large ingests and derivative generation. To accommodate growing repositories caching mechanisms, infrastructure changes, and code updates are necessary.
The presentation will explore customer case studies that demonstrate interim solutions and the extensive, ongoing research and development to find long-term solutions.
The document proposes a solution to replace inode-based storage with a key-value store mapping objects directly to positions in large "volumes" or files to address scalability issues. It benchmarks significantly better performance for puts, gets, and concurrent operations compared to an XFS filesystem, using less RAM and avoiding compaction costs. Open tasks include replication, erasure coding, and testing on object servers.
This document discusses hotspot garbage collection (GC) tuning. It begins by explaining generational heap spaces and different GC algorithms like G1. It emphasizes that GC tuning goals and solutions are application-dependent. It then provides guidance on tuning for throughput versus pause time and describes how to monitor GC using tools like VisualVM. Specific GC algorithms like CMS and options for each are explained. Finally, it discusses G1 GC phases and tuning techniques like increasing heap size and survivor space.
Wido den Hollander created AuroraObjects, a public object storage service using Ceph RADOS Gateway. AuroraObjects caches frequently accessed objects with Varnish to improve performance. Logstash and Elasticsearch are used to log all requests and perform usage accounting since requests bypass RADOS Gateway when cached. The Ceph cluster is configured with CRUSH to store each replica on a different power circuit for high availability in case of power failures.
This document summarizes the use of Gluster storage to provide persistent volumes for OpenShift.io. It describes how the initial EBS solution had limitations around volume attach limits and startup delays. A Gluster-based approach using a "supervol" volume with subdirectories for each PV addressed these issues and allowed overcommitting storage capacity. The solution evolved over time through configuration changes and lessons learned around performance and stability. Future plans include migrating Gluster storage into the OpenShift cluster for better data management and storage scalability.
This document provides an overview of key Kubernetes concepts including containers, pods, volumes, deployments, services, configmaps, secrets, replica sets, and horizontal pod autoscaling. It describes the basic building blocks in Kubernetes like pods, containers, volumes, labels and selectors. It also covers different types of services, deployments for declarative updates, replica sets for scaling pods, and horizontal pod autoscaling for autoscaling based on CPU utilization.
Choosing Right Garbage Collector to Increase Efficiency of Java Memory UsageJelastic Multi-Cloud PaaS
This document discusses various garbage collectors and their performance when used with Java applications. It provides an overview of garbage collectors like G1, Shenandoah, ZGC, OpenJ9, C4, ConcMarkSweep, Serial, Parallel, Epsilon and their configurations and performance. Load testing results of these garbage collectors are presented when run on Jelastic and Kubernetes. Methods to dynamically adjust Java heap size like -XX:SoftMaxHeapSize, -Xsoftmx and dynamic max memory limit are also covered.
Pierre Mavro from Criteo discussed Couchbase usage at their company. Criteo has over 100 Couchbase clusters storing over 90TB of data serving up to 25 million queries per second. They benchmarked Couchbase and found network bandwidth and replicas increased latency. To improve, Criteo monitored latency, split workloads across clusters, automated operations, and tuned Couchbase and systems. Their changes helped Couchbase scale for Criteo's large workload.
Apache Cassandra Lunch #67: Moving Data from Cassandra to Datastax AstraAnant Corporation
In Apache Cassandra Lunch #67, we discussed how to move data from Open Source Cassandra to Datastax Astra using dsbulk/scylla migratory.
https://github.com/DataStax-Examples/dsbulk-to-astra/
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-67-moving-data-from-cassandra-to-datastax-astra-with-dsbulk
Accompanying Youtube: https://youtu.be/0k7RBf5vi5M
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
[email protected]
LinkedIn:
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Twitter:
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Eventbrite:
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Remora is a lightweight alternative to ASDF for managing Lisp system definitions and dependencies. Benchmark tests show that Remora can load the Hunchentoot system faster than ASDF, taking 29.572 seconds versus 36.464 seconds for ASDF. However, Remora uses more memory, allocating 304,207,024 bytes compared to 313,210,864 bytes for ASDF. Running Remora's tasks in parallel can further reduce the load time for Hunchentoot to 12.257 seconds but increases memory allocation to 2,952,899,360 bytes.
In Apache Cassandra Lunch #59: Functions in Cassandra, we discussed the functions that are usable inside of the Cassandra database. The live recording of Cassandra Lunch, which includes a more in-depth discussion and a demo, is embedded below in case you were not able to attend live.
This document discusses scaling up logging and metrics in OpenShift Container Platform (OCP). It provides an overview of the logging stack including Elasticsearch, Fluentd, and Kibana. It also summarizes the metrics stack including Cassandra, Heapster, and Hawkular. The document outlines testing done to evaluate limits and scaling of these components on large OCP clusters with thousands of nodes and pods. It provides recommendations for configuring and deploying the infrastructure to support high throughput logging and metrics collection.
This document discusses garbage collection (GC) in the Hotspot Java Virtual Machine (JVM). It provides an overview of JVM structure and memory areas like the heap and method area. It then summarizes different GC algorithms used in the Hotspot JVM like the young generation GC, full GC, and parallel scavenge and mark-sweep compacting GCs. It also discusses GC tuning flags and tools like jstat and jvisualvm.
This document discusses memory related issues in Android applications. It explains that each app runs in a separate process with limited memory based on the device. If an app demands more memory than the limit, it will crash. Memory leaks and handling large bitmaps can also cause issues. Tools like logcat, MAT, and adb commands can help debug memory problems by analyzing heap dumps and tracking allocations over time.
1. The document discusses cloud object storage, describing its features like multipart uploads, versioning, and lifecycles. It provides examples of using object storage for media and documents.
2. Key aspects of object storage security are covered, including signatures, encryption, access control lists, and policies. Disaster recovery options like geo-replication are also summarized.
3. In the conclusion, the document emphasizes using object storage APIs to access advanced features, ensuring data safety, testing disaster recovery plans, and using Ceph for private cloud object storage.
MariaDB Auto-Clustering, Vertical and Horizontal Scaling within Jelastic PaaSJelastic Multi-Cloud PaaS
Availability and performance have a direct business impact for most of the companies nowadays. No one wants to lose money because of occasional downtime or data loss. Thus, to minimize the risk and ensure an extra level of redundancy, clustering and automatic scaling should be used. In this video Ruslan Synytsky presented how Jelastic PaaS implemented auto-clustering of MariaDB by providing the customers with different replication options out-of-box with no need in manual configurations. It is also detailed how to automate vertical and horizontal scaling of databases running in the cloud.
Video recording of the session https://www.youtube.com/watch?v=6MND3feb5zM
Gluster can provide block storage using LIO/TCMU. It was demonstrated providing an iSCSI block device from a Gluster volume, including block snapshots. Performance numbers were shown and it can integrate with containers by providing a persistent block device. Kubernetes was demonstrated using Gluster block storage by having nodes initiate iSCSI sessions to access a target device mounted in pods. Future work may include more testing, Heketi integration for provisioning, and hyper-convergence.
KDB database (EPAM tech talks, Sofia, April, 2015)Martin Toshev
KDB is an in-memory column-oriented database that provides high-performance for real-time and historical large volumes of data. It is used widely in the financial industry. KDB supports the Q programming language for querying and manipulating data, and can be deployed in a distributed environment. The Java API provides simple connection and query methods to access a KDB database. KDB is well-suited for use cases involving capturing market data feeds and analyzing FIX messages.
Kdb+ is a database and analytics software designed for processing large, diverse financial and market data in real-time. It uses the q programming language which allows for less code and faster execution compared to other languages. Kdb+ includes components for ingesting data from multiple sources, storing real-time and historical data, performing analytics using triggers without slowing performance, and returning query results in various formats. The q language provides SQL-like and time series querying capabilities along with built-in functions to minimize programming and data transferred over networks for efficient analytics. Users can get started with kdb+ by downloading and installing it, then executing queries from the command line or IDE.
Scaling Jakarta EE Applications Vertically and Horizontally with Jelastic PaaSJelastic Multi-Cloud PaaS
In this presentation, you'll find out what metrics should be tracked in order to meet the load requirements of application, how to finetune scaling triggers in order to efficiently handle different load levels, how to automate vertical and horizontal scaling of Jakarta EE applications running in the cloud.
Also, we share how to integrate load performance testing tools for adjusting horizontal scaling and making sure that your application can cope with production workloads.
Practical side is shown based on Jelastic PaaS https://jelastic.com/
The Java memory model and the Garbage Collector can drive you into serious problems if you don't know how it runs, defrags, and remove objects - this presentation is not updated for Java 8.
Presentation held at GRNET Digital Technology Symposium on November 5-6, 2018 at the Stavros Niarchos Foundation Cultural Center, Athens, Greece.
• Introduction to Ceph and its internals
• Presentation of GRNET's Ceph deployments (technical specs, operations)
• Usecases: ESA Copernicus, ~okeanos, ViMa
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
Rob Skillington gave a presentation on observability and M3, Uber's open source time series database. Some key points:
- M3 was created at Uber to handle high dimensionality metrics at massive scale, storing over 11 billion unique time series.
- It uses techniques like Roaring Bitmaps to efficiently store and query metrics with many dimensions or tag values.
- M3 can ingest metrics from Prometheus and Graphite, storing over 33 million metrics per second while powering dashboards and 150,000 alerts.
- The open source M3DB component can run standalone or on Kubernetes, providing a scalable time series storage solution for complex monitoring needs.
An Introduction to JVM Internals and Garbage Collection in JavaAbhishek Asthana
This document provides an overview of Java memory structures and garbage collection. It discusses the key areas of memory used by the JVM - heap, method area, native area, and threads. It then covers garbage collection concepts like roots, algorithms like mark-sweep-compact, and different GC strategies like serial, parallel, concurrent mark-sweep, and Garbage First collector. Performance metrics for evaluating GC and how objects transition between generations in generational collection are also summarized.
This document describes a testing framework for analyzing Java garbage collection (GC) performance. It consists of:
1. A properties file that specifies test parameters like the GC algorithm, heap size, and object lifetimes.
2. A script file that defines the sequence of object creations and workload.
3. Classes that execute the script, measure GC performance, and write output to log files.
4. A script that iterates the tests, varying a property each time and analyzing the results.
English version of the presentation we gave at Devoxx FR 2012.
In depth analysis on how java Garbage collector works and how to minimise pause in your application.
Java garbage collection has evolved significantly since its inception in 1959. The modern Hotspot JVM uses generational garbage collection with a young and old generation. It employs concurrent and parallel techniques like CMS to minimize pauses. OutOfMemoryErrors require increasing heap sizes or fixing leaks. Finalizers are generally avoided due to performance impacts. GC tuning must be tested under realistic loads rather than one-size-fits-all settings. Analysis tools help correlate GC logs with application behavior.
Choosing Right Garbage Collector to Increase Efficiency of Java Memory UsageJelastic Multi-Cloud PaaS
This document discusses various garbage collectors and their performance when used with Java applications. It provides an overview of garbage collectors like G1, Shenandoah, ZGC, OpenJ9, C4, ConcMarkSweep, Serial, Parallel, Epsilon and their configurations and performance. Load testing results of these garbage collectors are presented when run on Jelastic and Kubernetes. Methods to dynamically adjust Java heap size like -XX:SoftMaxHeapSize, -Xsoftmx and dynamic max memory limit are also covered.
Pierre Mavro from Criteo discussed Couchbase usage at their company. Criteo has over 100 Couchbase clusters storing over 90TB of data serving up to 25 million queries per second. They benchmarked Couchbase and found network bandwidth and replicas increased latency. To improve, Criteo monitored latency, split workloads across clusters, automated operations, and tuned Couchbase and systems. Their changes helped Couchbase scale for Criteo's large workload.
Apache Cassandra Lunch #67: Moving Data from Cassandra to Datastax AstraAnant Corporation
In Apache Cassandra Lunch #67, we discussed how to move data from Open Source Cassandra to Datastax Astra using dsbulk/scylla migratory.
https://github.com/DataStax-Examples/dsbulk-to-astra/
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-67-moving-data-from-cassandra-to-datastax-astra-with-dsbulk
Accompanying Youtube: https://youtu.be/0k7RBf5vi5M
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
[email protected]
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Remora is a lightweight alternative to ASDF for managing Lisp system definitions and dependencies. Benchmark tests show that Remora can load the Hunchentoot system faster than ASDF, taking 29.572 seconds versus 36.464 seconds for ASDF. However, Remora uses more memory, allocating 304,207,024 bytes compared to 313,210,864 bytes for ASDF. Running Remora's tasks in parallel can further reduce the load time for Hunchentoot to 12.257 seconds but increases memory allocation to 2,952,899,360 bytes.
In Apache Cassandra Lunch #59: Functions in Cassandra, we discussed the functions that are usable inside of the Cassandra database. The live recording of Cassandra Lunch, which includes a more in-depth discussion and a demo, is embedded below in case you were not able to attend live.
This document discusses scaling up logging and metrics in OpenShift Container Platform (OCP). It provides an overview of the logging stack including Elasticsearch, Fluentd, and Kibana. It also summarizes the metrics stack including Cassandra, Heapster, and Hawkular. The document outlines testing done to evaluate limits and scaling of these components on large OCP clusters with thousands of nodes and pods. It provides recommendations for configuring and deploying the infrastructure to support high throughput logging and metrics collection.
This document discusses garbage collection (GC) in the Hotspot Java Virtual Machine (JVM). It provides an overview of JVM structure and memory areas like the heap and method area. It then summarizes different GC algorithms used in the Hotspot JVM like the young generation GC, full GC, and parallel scavenge and mark-sweep compacting GCs. It also discusses GC tuning flags and tools like jstat and jvisualvm.
This document discusses memory related issues in Android applications. It explains that each app runs in a separate process with limited memory based on the device. If an app demands more memory than the limit, it will crash. Memory leaks and handling large bitmaps can also cause issues. Tools like logcat, MAT, and adb commands can help debug memory problems by analyzing heap dumps and tracking allocations over time.
1. The document discusses cloud object storage, describing its features like multipart uploads, versioning, and lifecycles. It provides examples of using object storage for media and documents.
2. Key aspects of object storage security are covered, including signatures, encryption, access control lists, and policies. Disaster recovery options like geo-replication are also summarized.
3. In the conclusion, the document emphasizes using object storage APIs to access advanced features, ensuring data safety, testing disaster recovery plans, and using Ceph for private cloud object storage.
MariaDB Auto-Clustering, Vertical and Horizontal Scaling within Jelastic PaaSJelastic Multi-Cloud PaaS
Availability and performance have a direct business impact for most of the companies nowadays. No one wants to lose money because of occasional downtime or data loss. Thus, to minimize the risk and ensure an extra level of redundancy, clustering and automatic scaling should be used. In this video Ruslan Synytsky presented how Jelastic PaaS implemented auto-clustering of MariaDB by providing the customers with different replication options out-of-box with no need in manual configurations. It is also detailed how to automate vertical and horizontal scaling of databases running in the cloud.
Video recording of the session https://www.youtube.com/watch?v=6MND3feb5zM
Gluster can provide block storage using LIO/TCMU. It was demonstrated providing an iSCSI block device from a Gluster volume, including block snapshots. Performance numbers were shown and it can integrate with containers by providing a persistent block device. Kubernetes was demonstrated using Gluster block storage by having nodes initiate iSCSI sessions to access a target device mounted in pods. Future work may include more testing, Heketi integration for provisioning, and hyper-convergence.
KDB database (EPAM tech talks, Sofia, April, 2015)Martin Toshev
KDB is an in-memory column-oriented database that provides high-performance for real-time and historical large volumes of data. It is used widely in the financial industry. KDB supports the Q programming language for querying and manipulating data, and can be deployed in a distributed environment. The Java API provides simple connection and query methods to access a KDB database. KDB is well-suited for use cases involving capturing market data feeds and analyzing FIX messages.
Kdb+ is a database and analytics software designed for processing large, diverse financial and market data in real-time. It uses the q programming language which allows for less code and faster execution compared to other languages. Kdb+ includes components for ingesting data from multiple sources, storing real-time and historical data, performing analytics using triggers without slowing performance, and returning query results in various formats. The q language provides SQL-like and time series querying capabilities along with built-in functions to minimize programming and data transferred over networks for efficient analytics. Users can get started with kdb+ by downloading and installing it, then executing queries from the command line or IDE.
Scaling Jakarta EE Applications Vertically and Horizontally with Jelastic PaaSJelastic Multi-Cloud PaaS
In this presentation, you'll find out what metrics should be tracked in order to meet the load requirements of application, how to finetune scaling triggers in order to efficiently handle different load levels, how to automate vertical and horizontal scaling of Jakarta EE applications running in the cloud.
Also, we share how to integrate load performance testing tools for adjusting horizontal scaling and making sure that your application can cope with production workloads.
Practical side is shown based on Jelastic PaaS https://jelastic.com/
The Java memory model and the Garbage Collector can drive you into serious problems if you don't know how it runs, defrags, and remove objects - this presentation is not updated for Java 8.
Presentation held at GRNET Digital Technology Symposium on November 5-6, 2018 at the Stavros Niarchos Foundation Cultural Center, Athens, Greece.
• Introduction to Ceph and its internals
• Presentation of GRNET's Ceph deployments (technical specs, operations)
• Usecases: ESA Copernicus, ~okeanos, ViMa
FOSDEM 2019: M3, Prometheus and Graphite with metrics and monitoring in an in...Rob Skillington
Rob Skillington gave a presentation on observability and M3, Uber's open source time series database. Some key points:
- M3 was created at Uber to handle high dimensionality metrics at massive scale, storing over 11 billion unique time series.
- It uses techniques like Roaring Bitmaps to efficiently store and query metrics with many dimensions or tag values.
- M3 can ingest metrics from Prometheus and Graphite, storing over 33 million metrics per second while powering dashboards and 150,000 alerts.
- The open source M3DB component can run standalone or on Kubernetes, providing a scalable time series storage solution for complex monitoring needs.
An Introduction to JVM Internals and Garbage Collection in JavaAbhishek Asthana
This document provides an overview of Java memory structures and garbage collection. It discusses the key areas of memory used by the JVM - heap, method area, native area, and threads. It then covers garbage collection concepts like roots, algorithms like mark-sweep-compact, and different GC strategies like serial, parallel, concurrent mark-sweep, and Garbage First collector. Performance metrics for evaluating GC and how objects transition between generations in generational collection are also summarized.
This document describes a testing framework for analyzing Java garbage collection (GC) performance. It consists of:
1. A properties file that specifies test parameters like the GC algorithm, heap size, and object lifetimes.
2. A script file that defines the sequence of object creations and workload.
3. Classes that execute the script, measure GC performance, and write output to log files.
4. A script that iterates the tests, varying a property each time and analyzing the results.
English version of the presentation we gave at Devoxx FR 2012.
In depth analysis on how java Garbage collector works and how to minimise pause in your application.
Java garbage collection has evolved significantly since its inception in 1959. The modern Hotspot JVM uses generational garbage collection with a young and old generation. It employs concurrent and parallel techniques like CMS to minimize pauses. OutOfMemoryErrors require increasing heap sizes or fixing leaks. Finalizers are generally avoided due to performance impacts. GC tuning must be tested under realistic loads rather than one-size-fits-all settings. Analysis tools help correlate GC logs with application behavior.
Вячеслав Блинов «Java Garbage Collection: A Performance Impact»Anna Shymchenko
This document discusses Java garbage collection and its performance impact. It provides an overview of garbage collection, including that garbage collectors reclaim memory from objects no longer in use. It describes the different Java GC algorithms like serial, parallel, CMS, and G1 collectors and how to choose between them based on factors like heap size and CPU availability. It also gives guidance on basic GC tuning techniques like sizing the heap and generations as well as using adaptive sizing controls.
This document discusses Java concurrency and the Java memory model. It begins with an agenda that covers the Java memory model, thread confinement, the Java atomic API, immutable objects, and memory consumption. It then goes into more detail on the Java memory model, discussing topics like ordering, visibility, and atomicity. It provides examples and references to help understand concepts like sequential consistency and data races. It also covers thread confinement techniques like ad hoc confinement, stack confinement, and using ThreadLocal.
Николай Папирный Тема: "Java memory model для простых смертных"Ciklum Minsk
This document provides an overview of the Java Memory Model (JMM). It begins by explaining why developers should learn about the JMM and covers key concepts like program order, sequential consistency, synchronization actions, synchronization order, happens-before relationships, and double checked locking. The document uses examples and diagrams to illustrate these concepts and how the JMM handles issues like visibility and atomicity in multithreaded programs. It aims to explain the essential aspects of the JMM in an accessible way for developers.
The Java Memory Model describes how threads interact with shared memory in Java programs. It allows compiler optimizations but also provides constructs like synchronized, volatile, and final to establish "happens-before" ordering between threads and ensure visibility and atomicity of memory operations. The model is designed to enable both efficient multithreaded execution and correct synchronization in user code.
This document discusses the Java Memory Model (JMM). It begins by introducing the goals of familiarizing the attendee with the JMM, how processors work, and how the Java compiler and JVM work. It then covers key topics like data races, synchronization, atomicity, and examples. The document provides examples of correctly synchronized programs versus programs with data races. It explains concepts like happens-before ordering, volatile variables, and atomic operations. It also discusses weaknesses in some common multi-threading constructs like double-checked locking and discusses how constructs like final fields can enable safe publication of shared objects. The document concludes by mentioning planned improvements to the JMM in JEP 188.
Java Memory Consistency Model - concepts and contextTomek Borek
Java Memory Consistency Model is a difficult topic.
It's useful in making sure that multi-threaded programs on multi-threaded cores will interact with each other (and through memory) in a consistent manner.
It's specification is damn hard (even according to folks with lots of concurrent experience, like Doug Lea) to read, understand and routinely follow without error.
This presentation talks about some fallacies surrounding memory model, explains it, offers definitions and reasons for it's existence. It ain't deep, it's more entry level stuff.
This presentation is primarily based on Oracle's "Java SE 6 HotSpot™ Virtual Machine Garbage Collection Tuning" document.
This introduces how Java manages memory using generations, available garbage collectors and how to tune them to achieve desired performance.
This document discusses the Java Memory Model (JMM) and how it describes how threads interact through memory in Java. It covers key aspects of the JMM including happens-before ordering, memory barriers, visibility rules, and how final fields and atomic instructions interact with the memory model. It also discusses performance considerations and how different processor architectures implement memory ordering.
The Java Memory Model defines rules for how threads interact through shared memory in Java. It specifies rules for atomicity, ordering, and visibility of memory operations. The JMM provides guarantees for code safety while allowing compiler optimizations. It defines a happens-before ordering of instructions. The ordering rules and visibility rules ensure threads see updates from other threads as expected. The JMM implementation inserts memory barriers as needed to maintain the rules on different hardware platforms.
The workshop is based on several Nikita Salnikov-Tarnovski lectures + my own research. The workshop consists of 2 parts. The first part covers:
- different Java GCs, their main features, advantages and disadvantages;
- principles of GC tuning;
- work with GC Viewer as tool for GC analysis;
- first steps tuning demo;
- comparison primary GCs on Java 1.7 and Java 1.8
The second part covers:
- work with Off-Heap: ByteBuffer / Direct ByteBuffer / Unsafe / MapDB;
- examples and comparison of approaches;
The off-heap-demo: https://github.com/moisieienko-valerii/off-heap-demo
Nowadays people usually talk more about big data, internet of things, and other buzzwords on various conferences. However, sometimes developers tend to not pay enough attention to the core things such as garbage collection. After having a short discussion with many somewhat experienced Java developers I came to a conclusion that most of them do not know how many garbage collectors there are in the latest JVM, and under what circumstances each of them should be enabled. This presentation is aimed to improve or refresh people’s knowledge on this core topic, and share a real use case when it helped us to resolve production issue.
At first glance, writing concurrent programs in Java seems like a straight-forward task. But the devil is in the detail. Fortunately, these details are strictly regulated by the Java memory model which, roughly speaking, decides what values a program can observe for a field at any given time. Without respecting the memory model, a Java program might behave erratic and yield bugs that only occure on some hardware platforms. This presentation summarizes the guarantees that are given by Java's memory model and teaches how to properly use volatile and final fields or synchronized code blocks. Instead of discussing the model in terms of memory model formalisms, this presentation builds on easy-to follow Java code examples.
GC Tuning in the HotSpot Java VM - a FISL 10 PresentationLudovic Poitou
This document provides a summary of a presentation on garbage collection tuning in the Java HotSpot Virtual Machine. It introduces the presenters and their backgrounds in GC and Java performance. The main points covered are that GC tuning is an art that requires experience, and tuning advice is provided for the young generation, Parallel GC, and Concurrent Mark Sweep GC. Monitoring GC performance and avoiding fragmentation are also discussed.
This document discusses tuning garbage collection in the Java Virtual Machine. It describes key metrics for measuring garbage collection performance like throughput, footprint, and pause times. Factors that impact these metrics like generation sizing, survivor space ratios, and garbage collector selection are explained. The document also provides guidance on using JVM flags and garbage collection logs to analyze and improve performance.
This presentation was given to the system adminstration team to give them an idea of how GC works and what to look for when there is abottleneck and troubles.
Quick introduction to Java Garbage Collector (JVM GC)Marcos García
The document discusses the Java Virtual Machine (JVM) and garbage collection. It describes the JVM memory areas like the heap, stack, method area and PC registers. It explains garbage collection techniques like minor and major GC used for young and old generation memory. The document also discusses different garbage collectors like serial, parallel, CMS and G1 collectors and how they perform compaction to reduce heap fragmentation. Tools for monitoring heap usage like jconsole and Websphere resource analyzer are also mentioned.
Java is finally elastic! OpenJDK improvements and new features in Garbage Collection technology resulted in enhancing Java vertical scaling and resource consumption. Now JVM can promptly return unused memory and, as result it can go up and down automatically. In this presentation, we cover the main achievements in vertical scaling direction, as well as share peculiarities and tuning details of different GCs. Find out how to make your Java environments more elastic to follow the load and lower down the total cost of ownership at a large scale.
The document discusses garbage collection in the Java Virtual Machine. It outlines the runtime data areas of JVM including heap, stacks, and method area. It describes different approaches to determining if an object is live or dead including reference counting and tracing GC roots. The document then explains common garbage collection algorithms like mark-sweep and copying and how different garbage collectors in Hotspot JVM like Serial, Parallel, and CMS work. It also discusses policies for object allocation and handling promotion of objects.
This document discusses Java garbage collection. It describes the serial, parallel, and concurrent collectors. The serial collector uses one thread for garbage collection while the parallel collector performs minor collections in parallel. Concurrent collectors like CMS and G1 perform most work concurrently to minimize pauses. The document also discusses selecting collectors based on data size, performance priorities, and pause time requirements. It notes young generation collectors like Copy and PS Scavenge as well as old generation collectors like MarkSweepCompact and ConcurrentMarkSweep.
Have you ever seen an OutOfMemoryError? I'm sure you have. But then, did you understood that line you copied from StackOverflow?
If you haven't, and if you want a gently introduction to the complex world of Java Garbage Collection this is your talk.
I'll talk about garbage collection concepts, the garbage collection in the Hotspot JVM (the default in Oraclel's JDK/JRE) and I'll try to put it in terms that any Java developer can grasp. The next time you'll face the dreaded 'OutOfMemoryError', at least, you'll know what are you up against.
What to do in case in which an application does not provide the desired performance? If you have ever had problems with optimizing the performance of Java applications, surely you had to invest a solid amount of time to find out the real cause for the problems, which included the involvement of administrators and developers. Is there a way to shorten the time required to find a solution, what free tools are available for this purpose and to check that you have finally solved the problem? In this presentation, we will try to provide answers to these questions with concrete real life examples.
The document provides information on application performance tuning education. It discusses key performance metrics like TPS and considerations for CPU usage, memory usage, garbage collection. It then summarizes Java/Tomcat performance tuning factors and garbage collection options. The last part discusses Java profiling and troubleshooting tools like JDK tools, HPROF, jhat, jmap, jstack, jstat and jvisualvm. It also provides an example Tomcat shell script configuration for setting JVM options and using profiling agents.
Elastic JVM for Scalable Java EE Applications Running in Containers #Jakart...Jelastic Multi-Cloud PaaS
Being configured smartly, Java can be scalable and cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. During this session, we will share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC and OpenJ9 garbage collectors while scaling Java EE applications vertically will help you to make the right choice for own projects.
More details about Garbage Collector types https://jelastic.com/blog/garbage-collection/
Free registration at Jelastic https://jelastic.com/
This document provides an overview of Java Virtual Machine (JVM) concepts including the Java process life cycle, class loading, JVM memory layout, garbage collection, and tools for monitoring JVM performance and debugging issues. Key topics covered include the Java main method, class loader hierarchy, object size calculation, generational garbage collection, and commands like jinfo, jstack, jstat, jmap for viewing thread dumps, GC statistics, and heap information.
This document discusses thread dumps, heap dumps, and best practices for generating and analyzing them. It defines a thread dump as a snapshot of all Java threads with information like name, state, and ID. A heap dump snapshots the Java heap showing object references and usage. The document recommends generating thread/heap dumps when full GC is frequent, memory isn't being reclaimed, or response times decrease. It provides commands to generate dumps and JVM options for tuning garbage collection and heap size. Finally, it includes links for further reading on Java memory management and performance tuning.
Virtual machines don't have to be slow, they don't even have to be slower than running native code.
All you have to do is write your code, lay back and let the JVM do its magic !
Learn about various JVM runtime optimizations and why is it considered one of the best VMs in the world.
ApacheCon2010: Cache & Concurrency Considerations in Cassandra (& limits of JVM)srisatish ambati
Cache & Concurrency considerations for a high performance Cassandra deployment.
SriSatish Ambati
Cassandra has hit it's stride as a distributed java NoSQL database! It's fast, it's in-memory, it's scalable, it's seda; It's eventually consistent model makes it practical for the large & growing volumes of unstructured data usecases. It is also time to run it through the filters of performance analysis. For starters it runs on the java virtual machine and inherits the capabilities and culpabilities of the platform. This presentation reviews the runtime architecture, cache behavior & performance of a real-world workload on Cassandra. We blend existing system & jvm tools to get a quick overview & a breakdown of hotspots in the get, put & update operations. We highlight the role played by garbage collection & fragmentation due to long lived objects; We investigate lock contention in the data structures under concurrent usage. Cassandra uses UDP for management & TCP for data: we look at robustness of the communication patterns during high spikes and cluster-wide events. We review Non-Blocking Hashmap modifications to Cassandra that improve concurrency & amplify performance of this frontrunner in the NoSQL space
ApacheCon2010 NA
Wed, 03 November 2010 15:00
cassandra
The document discusses Java memory management and garbage collection. It explains that the garbage collector manages Java memory and object allocation. Selecting the right garbage collection algorithm and properly sizing it is important for application performance. The document also provides tips on optimizing Java configuration such as configuring garbage collection logging, analyzing logs, tuning heap memory size, and taking heap dumps for troubleshooting.
Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)
Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
The role of the lexical analyzer
Specification of tokens
Finite state machines
From a regular expressions to an NFA
Convert NFA to DFA
Transforming grammars and regular expressions
Transforming automata to grammars
Language for specifying lexical analyzers
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
its all about Artificial Intelligence(Ai) and Machine Learning and not on advanced level you can study before the exam or can check for some information on Ai for project
The Fluke 925 is a vane anemometer, a handheld device designed to measure wind speed, air flow (volume), and temperature. It features a separate sensor and display unit, allowing greater flexibility and ease of use in tight or hard-to-reach spaces. The Fluke 925 is particularly suitable for HVAC (heating, ventilation, and air conditioning) maintenance in both residential and commercial buildings, offering a durable and cost-effective solution for routine airflow diagnostics.
Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754
LiDAR-Based System for Autonomous Cars
Autonomous Driving with LiDAR Tech
LiDAR Integration in Self-Driving Cars
Self-Driving Vehicles Using LiDAR
LiDAR Mapping for Driverless Cars
"Feed Water Heaters in Thermal Power Plants: Types, Working, and Efficiency G...Infopitaara
A feed water heater is a device used in power plants to preheat water before it enters the boiler. It plays a critical role in improving the overall efficiency of the power generation process, especially in thermal power plants.
🔧 Function of a Feed Water Heater:
It uses steam extracted from the turbine to preheat the feed water.
This reduces the fuel required to convert water into steam in the boiler.
It supports Regenerative Rankine Cycle, increasing plant efficiency.
🔍 Types of Feed Water Heaters:
Open Feed Water Heater (Direct Contact)
Steam and water come into direct contact.
Mixing occurs, and heat is transferred directly.
Common in low-pressure stages.
Closed Feed Water Heater (Surface Type)
Steam and water are separated by tubes.
Heat is transferred through tube walls.
Common in high-pressure systems.
⚙️ Advantages:
Improves thermal efficiency.
Reduces fuel consumption.
Lowers thermal stress on boiler components.
Minimizes corrosion by removing dissolved gases.
3. Overview of java garbage collectors
• Live objects vs Dead objects
• Runs automatically by JVM
• Cannot force JVM to run gc (System.gc())
• Demon thread called garbage collector
• Involves 3 steps
– Mark
– Sweep/Delete
– Compacting / defragmentation (time consuming)
• Minor garbage collection vs Major garbage
collection
4. Heap concepts
• Heap is divided in different sections
– Young generation
• Eden space
• Survivor space from
• Survivor space to
– Old generation (tenured space)
• Use for caching and long term survivor objects
6. Garbage collector types
• Serial collector
– Single thread shared between application and GC
– Used for small heap
– Used for less responsive applications
• Concurrent collector
– Runs concurrent to your application,
– doesn’t wait for old generation to be full
– Pause application during mark operation
– Used for low pause application
• Parallel collector
– Uses multiple CPU cores to perform GC
– Uses multiple threads for mark, sweep and fragmentation operations
– Wait for old generation to be full
– Pause application during all operations
– Used for batch processing or high throughput applications
• G1GC
– Highly customizable, can specify the time you want to run concurrent and parallel processor
– More garbage area collects first
– Option to specify maximum pause timings
8. JVM Tuning options
• -XX:+UseSerialGC
• -XX:+UseParallelGC
• -XX:+UseParallelOldGC
• -XX:+UseConcMarkSweepGC
• -XX:+UseG1GC
• -Xmsvalue : min amount of heap allocated
• -Xmxvalue : max amount of heap allocated
• -XX:NewRatio=ratio of young vs old generation
• -XX:NewSize=memory for eden space
• -XX:MaxNewSize=size memory for new generation space
• -XX:Permsize=size, used to define space for meta data and static objects
• -XX:+PrintGCDetails prints garbage collector details
• -Xloggc:gc.log where gc.log is a filename to store gc logs
• -verbose:gc for debugging gc logs